Data Science & AI Project Lead

About OGC

OGC is a boutique research and consulting firm specializing in data-driven strategy, AI-powered quantitative analytics, customer experience (CX) analytics, and innovation testing. We partner with Fortune 500 organizations across Financial Services, Specialty Retail, Telecommunications, Hospitality, Software, and Healthcare to uncover insights, improve decision-making, and drive measurable business impact. Our approach is hands-on and collaborative: deep discovery, advanced analytics using state-of-the-art tools, cross-functional problem solving, and executive-ready storytelling that turns insight into action.

Role Overview

We are seeking a Data Science & AI Project Lead to join our team and operate as an embedded expert within the Data Science and Innovation organization of a large, nationally scaled healthcare enterprise. This is a hybrid delivery role: part orchestration, part analytics leadership. You will lead the execution of a high-visibility AI and synthetic data strategy, while also remaining close enough to the work to credibly engage on modeling approaches, data pipelines, and analytical methodologies. You will manage numerous stakeholders including senior data scientists, product leaders, and executives, ensuring complex data science initiatives move from experimentation to production with clarity, governance, and momentum. This role is ideal for someone who is technically fluent in AI/ML and synthetic data, comfortable in regulated environments, and excels at influence, structure, and executive communication.

Key Responsibilities

• Lead end-to-end delivery of data science and AI initiatives, from scoping through production

• Facilitate requirements gathering and project scoping for high-priority AI, analytics, and synthetic data initiatives

• Own delivery cadence, prioritization, dependencies, and risk management across a multi-year AI roadmap

• Apply Agile, Scrum, or hybrid methodologies to maintain momentum and transparency

• Define and document technical specifications for synthetic data generation, modeling, and deployment

• Support execution of quantitative research and data science projects, including:

  • Data preparation and transformation

  • Modeling (classification, regression, forecasting, etc.)

  • Insight generation and reporting

• Oversee data pipeline flows and manage incoming stakeholder requests (data cuts, model refinements, ad-hoc analysis)

• Conduct functional QA to ensure analytical and synthetic data outputs meet accuracy, usability, and business requirements

• Serve as the primary liaison between technical teams and non-technical business, clinical, and operational stakeholders

• Translate complex data science progress into clear, credible executive narratives

• Build and deliver insights-based presentations and status updates to senior stakeholders

• Facilitate workshops and enablement sessions to drive adoption of AI tools and analytical outputs

• Support development of reusable data science products and internal accelerators

• Research and evaluate new data sources, tools, and methodologies

• Provide expert guidance on analytical techniques to analysts, data scientists, and sales leaders

• Help institutionalize best practices for AI governance, synthetic data use, and analytics at scale

Requirements

• BA/BS in Computer Science, Data Science, Business, or a related field

• MA/MS in Data Science, Computer Science, Business Analytics, or similar — an advantage

• 4+ years experience in data science, analytics, or AI-focused roles

• 3+ years experience leading or coordinating data science or AI projects

• Experience working with healthcare, life sciences, or regulated data environments — strong advantage

• Consulting or client-facing experience with senior stakeholders

• Machine Learning & statistical modeling

• Data pipelines and analytics workflows

• Python (Pandas, NumPy, Scikit-learn, XGBoost, PySpark, etc.)

• SQL

• Statistics and quantitative research methods

• Data visualization tools (Tableau, Power BI — advantage)

• Exceptional ability to translate technical detail into business value

• Strong analytical thinking and structured problem-solving

• Comfortable operating independently as an embedded resource

• Excellent written and spoken English (fluent and idiomatic)